Опубликована: Янв. 1, 2025
Язык: Английский
Scientific Reports, Год журнала: 2024, Номер 14(1)
Опубликована: Дек. 30, 2024
The study suggests a better multi-objective optimization method called 2-Archive Multi-Objective Cuckoo Search (MOCS2arc). It is then used to improve eight classical truss structures and six ZDT test functions. aims minimize both mass compliance simultaneously. MOCS2arc an advanced version of the traditional (MOCS) algorithm, enhanced through dual archive strategy that significantly improves solution diversity performance. To evaluate effectiveness MOCS2arc, we conducted extensive comparisons with several established algorithms: MOSCA, MODA, MOWHO, MOMFO, MOMPA, NSGA-II, DEMO, MOCS. Such comparison has been made various performance metrics compare benchmark efficacy proposed algorithm. These comprehensively assess algorithms' abilities generate diverse optimal solutions. statistical results demonstrate superior evidenced by Additionally, Friedman's & Wilcoxon's corroborate finding consistently delivers compared others. show highly effective improved algorithm for structure optimization, offering significant promising improvements over existing methods.
Язык: Английский
Процитировано
19
Results in Engineering, Год журнала: 2025, Номер 25, С. 103933 - 103933
Опубликована: Янв. 5, 2025
Язык: Английский
Процитировано
8
Evolutionary Intelligence, Год журнала: 2025, Номер 18(1)
Опубликована: Янв. 9, 2025
Язык: Английский
Процитировано
6
Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Янв. 8, 2025
Heart disease is a category of various conditions that affect the heart, which includes multiple diseases influence its structure and operation. Such may consist coronary artery disease, characterized by narrowing or clotting arteries supply blood to heart muscle, with resulting threat attacks. rhythm disorders (arrhythmias), valve problems, congenital defects present at birth, muscle (cardiomyopathies) are other types disease. The objective this work introduce Greylag Goose Optimization (GGO) algorithm, seeks improve accuracy classification. GGO algorithm's binary format specifically intended choose most effective set features can classification when compared six optimization algorithms. bGGO algorithm for selecting optimal enhance accuracy. phase utilizes many classifiers, findings indicated Long Short-Term Memory (LSTM) emerged as classifier, achieving an rate 91.79%. hyperparameter LSTM model tuned using GGO, outcome alternative optimizers. obtained highest performance, 99.58%. statistical analysis employed Wilcoxon signed-rank test ANOVA assess feature selection outcomes. Furthermore, visual representations results was provided confirm robustness effectiveness proposed hybrid approach (GGO + LSTM).
Язык: Английский
Процитировано
5
Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Фев. 9, 2025
In this study, we present a comparative analysis of various trajectory optimization algorithms for Unmanned Aerial Vehicles (UAVs) navigating complex environments. The performance the proposed FOPID-TID based HAOAROA (Hybrid Archimedes Optimization Algorithm-Rider Algorithm) is evaluated against traditional methods such as A*, JPS, Bezier, and L-BSGF algorithms. approach integrates advantages fractional-order control with hybrid techniques to improve UAV planning. Simulation results indicate that method carries significantly better than respect length, smoothness, overall stability. Remarkably, yields 10% reduced length smoother while also being more computationally efficient. By using parameters, dynamic response becomes in challenging This shows disturbance rejection precision are much superior original two subroutines. applications presented study allow future growth system improvements provide proof concept improving UAVs dynamic,
Язык: Английский
Процитировано
5
Alexandria Engineering Journal, Год журнала: 2025, Номер 120, С. 296 - 317
Опубликована: Фев. 18, 2025
Язык: Английский
Процитировано
5
Computers & Structures, Год журнала: 2025, Номер 308, С. 107647 - 107647
Опубликована: Янв. 8, 2025
Язык: Английский
Процитировано
4
Advances in Engineering Software, Год журнала: 2025, Номер 203, С. 103883 - 103883
Опубликована: Фев. 18, 2025
Язык: Английский
Процитировано
4
Results in Engineering, Год журнала: 2025, Номер unknown, С. 104241 - 104241
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
3
Journal of Optimization, Год журнала: 2024, Номер 2024(1)
Опубликована: Янв. 1, 2024
The multiobjective (MO) optimizers show great promise in solving constrained engineering structural problems. This paper introduces a MO version of the Brown Bear Optimization (BBO) algorithm, inspired by foraging behavior brown bears. proposed Multiobjective (MOBBO) algorithm is applied to five optimization problems, including 10‐bar, 25‐bar, 60‐bar, 72‐bar, and 942‐bar trusses, aiming minimize both mass maximum nodal deflection simultaneously. Comparative evaluations against six benchmark algorithms demonstrate MOBBO’s superior convergence, solution diversity, effectiveness addressing highly hypervolume (HV) inverted generational distance (IGD) metrics place MOBBO first rank according Friedman test, with an average standard deviation 0.0002. Moreover, spacing‐to‐extent (STE) (GD) second. final test highlights overall dominance, achieving rank. Best Pareto plots, diversity graphs, box plot analyses further suggest performance convergence compared existing algorithms. Therefore, can be effectively various tasks industry, offering refined global solutions contributing valuable insights field
Язык: Английский
Процитировано
12